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Hidden Markov Modeling for Radar Electronic Warfare

The growing proliferation and complexity of electromagnetic signals encountered in modern environments is greatly complicating the Electronic Support (ES), Electronic Attack (EA) and Electronic Intelligence (ELINT) functions. In conventional ES, EA and ELINT modeling, radar signals are expected to be stable or to correspond to radar modes. The concept examined in this report is the recognition of radar systems using hidden Markov modeling. The model is called hidden because the input symbols and state transitions of the radar system cannot be observed. What is observed is the output of the finite state automaton. Hidden Markov models have been used extensively, and very successfully, for speech recognition. Four electronic warfare problems are examined: (1) the classification problem: given an observation signal and multiple competing radar models, how do we choose the model which best matches the observation signal? (2) the decoding problem: given the observation sequence and the radar model, how do we choose a state sequence which in some way best explains the observations?